I Ranked 30 AI APIs By Price in 2026 — A Backend Dev's Notes
I've been paying LLM API bills long enough to develop a reflex — every time I see a new model launch, my first thought is "cool, but what does it cost per million tokens?" Not because I'm cheap (well, maybe a little), but because once you're shipping a product to real users, the difference between $0.25/M and $3.50/M output tokens is the difference between a profitable feature and one you quietly disable at 3 AM.
So I spent a weekend pulling verified pricing data from Global API for May 2026, ranked every model I could find by output cost, and made a bunch of tables so I don't have to think about it again for a while. Fwiw, this is the kind of post I wish someone had written for me six months ago.
Why Output Pricing Is the Number That Actually Matters
Most engineers I've talked to focus on input token pricing because it's intuitive — "how much does it cost to send a prompt?" But under the hood, output tokens are where the real money goes. Completions are typically 3-10× longer than prompts, and reasoning models (think DeepSeek-R1, Kimi K2.5) can blow through thousands of output tokens on a single hard query.
I learned this the hard way shipping a summarization feature where I assumed input costs dominated. Spoiler: they didn't. Output did. By a lot.
So every price I quote below is output dollars per 1M tokens, which is what I care about when I'm choosing a model for a real workload.
How I Pulled This Data
Global API exposes a pricing endpoint that returns every model in its catalog with both input and output rates. I scripted the whole thing rather than copy-pasting from marketing pages — never trust a vendor's "starting at" pricing imo.
Here's the snippet I used:
import httpx
import json
PRICING_URL = "https://global-apis.com/v1/pricing"
def fetch_pricing():
resp = httpx.get(PRICING_URL, timeout=30)
resp.raise_for_status()
return resp.json()
data = fetch_pricing()
models = sorted(
data["models"],
key=lambda m: m["output_per_million"]
)
for m in models[:10]:
print(f"{m['name']:30s} out=${m['output_per_million']:.2f}/M in=${m['input_per_million']:.2f}/M ctx={m['context_window']}")
This dumps the cheapest 10 models by output cost. The endpoint also returns provider metadata, context window sizes, and capabilities (vision, function calling, etc.), which is what I used to build the rankings below. All numbers in this post were verified as of May 20, 2026.
The Five Price Tiers
Before we get into individual models, it helps to bucket everything. I've found that most engineers I've worked with don't need to memorize 30 prices — they need to know which tier to start in.
| Tier | Output $/M | Sweet Spot For | Example Models |
|---|---|---|---|
| 🟢 Ultra-Budget | $0.01–$0.10 | Classification, routing, testing | Qwen3-8B, GLM-4-9B, Hunyuan-Lite |
| 🟡 Budget | $0.10–$0.30 | Prototyping, dev environments | DeepSeek V4 Flash, Qwen3-32B, Step-3.5-Flash |
| 🟠 Mid-Range | $0.30–$0.80 | Production apps, code generation | Hunyuan-Turbo, GLM-4.6, Doubao-Seed-Lite |
| 🔴 Premium | $0.80–$2.00 | Complex reasoning, enterprise | DeepSeek V4 Pro, MiniMax M2.5, GLM-5, Doubao-Seed-Pro |
| � Flagship | $2.00–$3.50 | Frontier thinking models | DeepSeek-R1, Kimi K2.5, Kimi K2.6, Qwen3.5-397B |
The range here is wild. A 350× cost difference between the cheapest and most expensive models on the same platform. That's not a rounding error — that's an architectural decision.
The Cheapest 30 Models, Ranked
Below is everything pulled from the pricing endpoint, sorted by output cost. Context window matters for some workloads, so I included it.
| Rank | Model | Provider | Out $/M | In $/M | Context |
|---|---|---|---|---|---|
| 1 | Qwen3-8B | Qwen | $0.01 | $0.01 | 32K |
| 2 | GLM-4-9B | GLM | $0.01 | $0.01 | 32K |
| 3 | Qwen2.5-7B | Qwen | $0.01 | $0.01 | 32K |
| 4 | GLM-4.5-Air | GLM | $0.01 | $0.07 | 32K |
| 5 | Qwen3.5-4B | Qwen | $0.05 | $0.05 | 32K |
| 6 | Hunyuan-Lite | Tencent | $0.10 | $0.39 | 32K |
| 7 | Qwen2.5-14B | Qwen | $0.10 | $0.05 | 32K |
| 8 | Step-3.5-Flash | StepFun | $0.15 | $0.13 | 32K |
| 9 | Qwen3.5-27B | Qwen | $0.19 | $0.33 | 32K |
| 10 | ByteDance-Seed-OSS | Doubao | $0.20 | $0.04 | 128K |
| 11 | Hunyuan-Standard | Tencent | $0.20 | $0.09 | 32K |
| 12 | Hunyuan-Pro | Tencent | $0.20 | $0.09 | 32K |
| 13 | ERNIE-Speed-128K | Baidu | $0.20 | $0.00 | 128K |
| 14 | Qwen3-14B | Qwen | $0.24 | $0.20 | 32K |
| 15 | DeepSeek V4 Flash | DeepSeek | $0.25 | $0.18 | 128K |
| 16 | Qwen3-32B | Qwen | $0.28 | $0.18 | 32K |
| 17 | Hunyuan-TurboS | Tencent | $0.28 | $0.14 | 32K |
| 18 | Ga-Economy | GA Routing | $0.13 | $0.18 | Auto |
| 19 | Qwen2.5-72B | Qwen | $0.40 | $0.20 | 128K |
| 20 | DeepSeek-V3.2 | DeepSeek | $0.38 | $0.35 | 128K |
| 21 | Doubao-Seed-Lite | ByteDance | $0.40 | $0.10 | 128K |
| 22 | Ling-Flash-2.0 | InclusionAI | $0.50 | $0.18 | 32K |
| 23 | Qwen3-VL-32B | Qwen | $0.52 | $0.26 | 32K |
| 24 | Qwen3-Omni-30B | Qwen | $0.52 | $0.30 | 32K |
| 25 | GLM-4-32B | GLM | $0.56 | $0.26 | 32K |
| 26 | Hunyuan-Turbo | Tencent | $0.57 | $0.18 | 32K |
| 27 | GLM-4.6V | GLM | $0.80 | $0.39 | 32K |
| 28 | Doubao-Seed-1.6 | ByteDance | $0.80 | $0.05 | 128K |
| 29 | Ga-Standard | GA Routing | $0.20 | $0.36 | Auto |
| 30 | DeepSeek V4 Pro | DeepSeek | $0.78 | $0.57 | 128K |
A few observations from staring at this table for too long:
Qwen and GLM dominate the floor. The four cheapest models all sit at $0.01/M output, and three of them are from these two vendors. If you're doing high-volume classification or routing, this is where the money is.
DeepSeek V4 Flash is the standout at $0.25/M. It punches way above its weight — 128K context, strong coding and reasoning benchmarks, and it's still in the budget tier. This is the model I keep coming back to.
128K context is becoming table stakes in the budget tier. ERNIE-Speed-128K at $0.20/M output with free input tokens is honestly kind of absurd.
GA Routing models are interesting. They auto-route between providers based on the query. The "Economy" tier at $0.13/M output is basically a meta-model — useful when you don't want to think about which backend to call.
Provider Breakdown: Who Owns Which Tier
DeepSeek — The Value King ($0.25–$2.50/M)
I keep coming back to DeepSeek. Their V4 Flash at $0.25/M output is, imo, the single best price-to-quality deal in the entire catalog right now. If you forced me to pick one model for a general-purpose production workload, I'd pick it without much hesitation.
Their premium tier (V4 Pro at $0.78/M, DeepSeek-R1 further up the flagship range) is where you go when reasoning matters more than cost. DeepSeek-R1 specifically is one of those models that uses a lot of output tokens — it thinks out loud — so the per-token cost compounds. But for hard problems, nothing else at this price point comes close.
Qwen — The Volume Play ($0.01–$0.52/M)
Qwen has, by my count, the most models in the cheap tier. Qwen3-8B, Qwen2.5-7B, Qwen3.5-4B — they're all under $0.05/M output. If you're doing something like spam classification, intent detection, or basic Q&A at scale, you can run these for essentially nothing.
The mid-range Qwen models (Qwen3-32B at $0.28/M, Qwen2.5-72B at $0.40/M) are where I start getting impressed. They handle real workloads and cost less than most people's coffee budget.
Tencent (Hunyuan Family) — Stable Mid-Range
Hunyuan-Lite at $0.10/M is a sneaky good budget option — better quality than the absolute floor models while staying in ultra-budget territory. Hunyuan-Turbo at $0.57/M is my go-to recommendation when someone wants a "boring reliable" mid-range model.
ByteDance (Doubao) — Long Context Champion
Doubao-Seed-OSS at $0.20/M output with 128K context and a $0.04/M input rate is genuinely absurd. If you're feeding in long documents and need decent output quality without going bankrupt, this is the one.
GLM — Quality at the Floor
GLM-4-9B and GLM-4.5-Air both at $0.01/M output. I've been running GLM-4-32B at $0.56/M in production for a code review tool and it's been solid.
The Flagship Tier — When Money Doesn't Matter
For the record, DeepSeek-R1, Kimi K2.5, Kimi K2.6, and Qwen3.5-397B all sit in the $2.00–$3.50/M output range. These are the "thinking" models — they chew through tokens internally before answering, which is why the per-token cost compounds. Use them for genuinely hard problems, not for "rewrite this sentence in a friendly tone."
A Routing Layer I Actually Use in Production
Once you have this many models, the obvious question is: do I really need to pick one? In practice, I've started routing requests based on difficulty. Easy stuff goes to Qwen3-8B at $0.01/M. Harder stuff goes to DeepSeek V4 Flash at $0.25/M. Only the genuinely gnarly reasoning queries hit DeepSeek-R1.
Here's a simplified version of my router:
import httpx
BASE_URL = "https://global-apis.com/v1"
TIER_CONFIG = {
"easy": {"model": "Qwen3-8B", "max_tokens": 256},
"medium": {"model": "DeepSeek V4 Flash", "max_tokens": 1024},
"hard": {"model": "DeepSeek-R1", "max_tokens": 4096},
}
def classify_difficulty(prompt: str) -> str:
if len(prompt) < 200:
return "easy"
if "prove" in prompt.lower() or "step by step" in prompt.lower():
return "hard"
return "medium"
def route_completion(prompt: str, api_key: str) -> str:
tier = classify_difficulty(prompt)
cfg = TIER_CONFIG[tier]
resp = httpx.post(
f"{BASE_URL}/chat/completions",
headers={"Authorization": f"Bearer {api_key}"},
json={
"model": cfg["model"],
"messages": [{"role": "user", "content": prompt}],
"max_tokens": cfg["max_tokens"],
},
timeout=60,
)
resp.raise_for_status()
return resp.json()["choices"][0]["message"]["content"]
This isn't sophisticated — it's basically a heuristic. But it's cut my LLM bill roughly in half versus sending everything to DeepSeek V4 Flash, and the easy queries are still fast.
My Actual Recommendations
If you just want the short version:
- For bulk / classification / routing: Qwen3-8B at $0.01/M. It's not glamorous, but it's basically free.
- For general production workloads: DeepSeek V4 Flash at $0.25/M. This is my default.
- For long-context document work: Doubao-Seed-OSS at $0.20/M with 128K context.
- For coding assistants: Qwen3-32B at $0.28/M or GLM-4-32B at $0.56/M.
- For multimodal: Qwen3-VL-32B at $0.52/M.
- For frontier reasoning: DeepSeek-R1, but budget for it.
Wrapping Up
The 2026 LLM market is, frankly, absurd in a good way. You can get GPT-4o-class quality for $0.25/M output now. You can run bulk classification at $0.01/M. The price gap that existed two years ago between "
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